Saeid Pourroostaei Ardakani

Dr. Saeid Pourroostaei Ardakani

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Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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29 March 2023 in Sustainability
Verified
Journal Article
Open access
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SASaeid Pourroostaei Ardakani
Saeid Pourroostaei Ardakani
XLXiangning Liang
Xiangning Liang
KMKal Tenna Mengistu
Kal Tenna Mengistu
RSRichard Sugianto So
Richard Sugianto So
XWXuhui Wei
Xuhui Wei
Ali CheshmehzangiAli Cheshmehzangi
Ali Cheshmehzangi
Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities.
6 April 2022 in Education and Information Technologies
Verified
Journal Article
Closed access
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XLXinyang Liu
Xinyang Liu
SASaeid Pourroostaei Ardakani
Saeid Pourroostaei Ardakani
20 February 2024 in IEEE Access
Verified
Journal Article
Open access
SMSheldon McCall
Sheldon McCall
SKShina Samuel Kolawole
Shina Samuel Kolawole
ANAfreen Naz
Afreen Naz
LGLiyun Gong
Liyun Gong
SASyed Waqar Ahmed
Syed Waqar Ahmed
SASaeid Pourroostaei Ardakani
Saeid Pourroostaei Ardakani
Falls bring about significant risks to individuals’ well-being and independence, prompting widespread public health concerns. Swift detection and even predicting the risk of falls are crucial for implementing effective measures to alleviate the adverse consequences associated with such incidents. This study presents a new framework for identifying and forecasting fall risks. Our approach utilizes a novel transformer model trained on 2D poses extracted through an off-the-shelf pose extractor, incorporating transfer learning techniques. Initially, the transformer is trained on a large dataset containing 2D poses of general actions. Subsequently, we freeze the majority of its layers and fine-tune only the last few layers using relatively smaller datasets for fall detection and prediction tasks. Experimental results indicate that our proposed method outperforms traditional machine learning (e.g., SVM, Decision Tree, etc.) and deep learning approaches (e.g., LSTM, CNN, ST-GCN, PoseC3D, etc.) in both fall detection and prediction tasks across various datasets.
17 March 2025 in Electronics
Verified
Journal Article
Open access
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MDMark Devine
Mark Devine
SASaeid Pourroostaei Ardakani
Saeid Pourroostaei Ardakani
MAMohammed Al-khafajiy
Mohammed Al-khafajiy
YJYvonne Kay James
Yvonne Kay James
Intrusion detection systems for internet-of-things devices are becoming more relevant as the international reliance on internet-of-things devices grows. Federated learning is one of the most promising areas of study in AI-driven intrusion detection systems in the internet of things and networking, being able to mitigate some of the more severe hardware requirements. Using a federated learning framework, we trained and evaluated several machine learning models to identify distributed denial-of-service attacks in IoT systems. Our framework introduces a novel approach to data preparation for federated learning, incorporating new processing techniques to maximise performance on real-world non-synthetic data. The results show that our proposed first-of-its-kind federated SVM model is highly effective for intrusion detection and matches or outperforms the benchmark algorithms in terms of the attack prediction accuracy, while demonstrating its feasibility for deployment on edge devices. We also compare the physical metrics to conduct one of the first comprehensive evaluations of model suitability for resource-constrained IoT networks, providing valuable insights into the trade-offs between detection accuracy and computational efficiency.
19 February 2023 in Journal of Ambient Intelligence and Humanized Computing
Verified
Journal Article
Open access
PDF file
SASaeid Pourroostaei Ardakani
Saeid Pourroostaei Ardakani
NDNanjiang Du
Nanjiang Du
CLChenhong Lin
Chenhong Lin
JYJiun-Chi Yang
Jiun-Chi Yang
ZBZhuoran Bi
Zhuoran Bi
LCLejun Chen
Lejun Chen
This article proposes a federated learning framework to build Random Forest, Support Vector Machine, and Linear Regression models for stock market prediction. The performance of the federated learning is compared against centralised and decentralised learning frameworks to figure out the best fitting approach for stock market prediction. According to the results, federated learning outperforms both centralised and decentralised frameworks in terms of Mean Square Error if Random Forest (MSE = 0.021) and Support Vector Machine techniques (MSE = 37.596) are used, while centralised learning (MSE = 0.011) outperforms federated and decentralised frameworks if a linear regression model is used. Moreover, federated learning gives a better model training delay as compared to the benchmarks if Linear Regression (time = 9.7 s) and Random Forest models (time = 515 s) are used, whereas decentralised learning gives a minimised model training delay (time = 3847 s) for Support Vector Machine.
20 February 2023 in Heliyon
Verified
Journal Article
Open access
Ali CheshmehzangiAli Cheshmehzangi
Ali Cheshmehzangi
ZSZhaohui Su
Zhaohui Su
RJRuoyu Jin
Ruoyu Jin
ADAyotunde Dawodu
Ayotunde Dawodu
Maycon SedrezMaycon Sedrez
Maycon Sedrez
Tong ZouTong Zou
Tong Zou
The spread of COVID-19 at a large scale and at a rapid pace indicates the lack of social distancing measures at multiple levels. The individuals are not to be blamed, nor should we assume the early measures were ineffective or not implemented. It is all down to the multiplicity of transmission factors that made the situation more complicated than initially anticipated. Therefore, in facing the COVID-19 pandemic, this overview paper discusses the importance of space in social distancing measures. The methods used to investigate this study are literature review and case study. Many scholarly works have already provided us with evidence-based models that suggest the influential role of social distancing measures in preventing COVID-19 community spread. To further elaborate on this important topic, the aim here is to look at the role of space not only at the individual level but at larger scales of communities, cities, regions, etc. The analysis helps better management of cities during the pandemics such as COVID-19. By reflecting on some of the ongoing research on social distancing, the study concludes with the role of space at multiple scales and how it is central to the practice of social distancing. We need to be more reflective and responsive to achieve earlier control and containment of the disease and the outbreak at the macro level.